The main objective of this study is to estimate the input
elasticities of production for poor and non-poor farms. The study
estimates the stochastic frontier production function. The results show
that the elasticities of production differ for poor and non-poor farms.
The production elasticity of land is substantially higher on rich farms
as compared to the farms belonging to poor farmers. This implies higher
returns on investment on land by the rich farmers. The salinity/sodicity
problem and the tail-end location of the plot adversely affect farm
productivity and efficiency, particularly at the poor farms. Moreover,
the average cost of the existence of technical inefficiencies is about
43 percent in terms of loss in output, with wide variations across farms
ranging from 17 percent to 62 percent. The study further concludes that
the least efficient group is not only operating far below the frontier
but it also operates at the lower portion of the production frontier.
Consequently, increasing access to the inputs would likely raise
productivity and reduce poverty.

The results imply that the land distribution using the notion of
land reforms in favour of poor/small farmers in the presence of existing
farm structure, rural infrastructure, and the weak farm-supporting
institutions is not expected to raise farm productivity and reduce
poverty among the poor farmers. The results call for a strong and active
role of the government in close partnership with the private sector to
initiate income-generating activities and inputs supply chains in the
rural areas to break the nexus of poverty, land degradation, and low
agricultural productivity.

1. INTRODUCTION

Agriculture sectors in less developed countries like Pakistan are
widely considered to play a vital role in the eradication of poverty. In
spite of the importance of the sector, the production potential in
agriculture in many of the developing countries is mostly unrealised due
mainly to under-investment in research and development, irrigation,
rural infrastructure, rural education, and health. Consequently, the
levels of productivity of the agriculture sector in these economies are
far below the potential that the developed countries achieved several
decades ago.

The multi-dimensional nature of the relationship between
agriculture and poverty is being acknowledged more widely. Higher
agricultural productivity affects family earnings and nutrition, which
in turn supports labour productivity and results in better health and
well-being of the people [Oshaug and Haddad (2002)]. Poor health of
workers either results in the loss of working days or reduces their
working capacity, leading to lower output [Croppenstedt and Muller
(2000)]. Poverty is likely to affect the capacity of the farm households
to avail themselves of better health and education facilities, to
purchase inputs at the proper time, to hold/acquire other farm assets,
to adopt new technologies and invest in conservation of their land
resources, etc. The low level of these factors in turn affects
agricultural productivity adversely. Therefore, poverty is not only an
effect but also a cause of low agricultural productivity. Thus, it is
imperative to pay more attention to this aspect of the relationship
between agricultural productivity and poverty.

The performance of the agriculture sector in Pakistan has been
satisfactory for over four decades. It grew at the rate of about 3.5
percent per year during the period 1959-60 to 2002-2003. The growth in
the crops sub-sector remained around 3.3 percent per year, while the
livestock sub-sector grew at a rate of more than 3.5 percent per annum
during the same period. Despite the fact that agriculture has been
growing at a reasonable rate, poverty has increased during the 1990s, in
sharp contrast to its declining trend in 1970s and 1980s [Amjad and
Kemal (1997); Ali and Tahir (1999); Jafri (1999); Arif, et al. (2001)].
In Pakistan, poverty has been generally higher in rural areas, and is
mostly concentrated among the landless and the small and tenant farmers.
It needs to be mentioned here that the rural indicators appeared to have
improved little in recent years, demonstrating that human deprivation in
the country is likely to deteriorate further in the future, forcing
people to stay poor [Arif and Ahmad (2001)]. There is a great likelihood
that the agricultural productivity situation will be affected adversely.

The poverty scenario in Pakistan shows that, generally, the growth
in agriculture sector has not benefited the poor sections of the
society. Rather, the fact is that the families which were not poor
earlier have been sliding down the poverty line. It points to the
possibility that the income inequality might have worsened due to the
deterioration in the distribution of productive assets and access to the
financial and other supporting institutions [Kemal (2003) and Timmer
(1997)]. If this is true, then the per capita income has to grow at a
much more rapid rate to make an impact in terms of reduction in poverty
[Kakwani (2001)].

Although growth and poverty are interlinked, the above discussion
highlights that the former is not a sufficient activity to reduce
poverty. To understand this phenomenon in the context of Pakistan, we
need to consider the main factors that acted as a driving force for
triggering growth in the agriculture sector, and we need to analyse who
is being benefited, and how. The key factors include: (1) the higher use
of conventional inputs, (2) increase in total factor productivity (TFP),
and (3) the targeted transformations in the institutional set-up that
assist the agriculture sector. These sources of growth are
inter-related, and who gets the benefits depends on the distribution of
assets, particularly the land.

Pakistan has a highly skewed distribution of farm lands, (1) and
the access to input and output markets is mainly determined by the
ownership of this factor of production. It is believed that benefits of
agricultural growth have also been unequally distributed. The poor small
farmers under-utilise various factors of production, particularly the
purchased inputs, because of financial constraints, which results in
lower productivity and income. Consequently, the poor farmers seemed to
be operating not only at the lower portion of the production frontier
but also appeared to be realising less than the maximum achievable
output with the given level of inputs. That must lead to rise in
poverty. (2)

The key factors behind the TFP, the second source of growth, are
agricultural research and extension, better rural infrastructure like
roads, electricity, education, and irrigation [Fan, Hazell and Thorat
(1999); Fan, Zhang and Zhang (2000); Evenson, Pray and Rosegrant (1999);
Rosegrant and Evenson (1993); and Ali (2000)]. The empirical literature
shows that the poor farmers have limited access to such facilities
[Iqbal, Khan and Ahmad (2002) and Ahmad, Chaudhry and Iqbal (2002)].
Consequently, the poor farmers are not in a position to benefit from
growth in TFP to the extent to which the rich farmers do. This results
in widening the gap between the poor/small and the rich/progressive
farmers. The third major factor, which could be instrumental for
agricultural growth is the policy-targeted institutional changes
including agricultural extension, education and credit, and improvement
in the functioning of input and output markets [Saris (2001)]. These
institutions have worsened the disparity between the rich/large and the
poor/small farmers in rural Pakistan by offering greater access to
influential and well-off farmers. Moreover, the agricultural price
policies in Pakistan have remained unfriendly to producers and tended to
slow down the growth [Lele (1989); Schiff and Valdes (1991) and Saris
(2001)].

As part of structural adjustment and stabilisation programmes, the
Government of Pakistan removed all subsidies during the 1990s, which
resulted in a manifold increase in input prices and, thus, greater cost
of production [Ahmad (2003)]. To compensate for the higher production
cost, the Government has been following the policy of increasing the
support prices of major crops. However, the increase in inputs prices
has been much faster than the compensation in terms of higher output
prices. Such trends in prices have been squeezing the profitability of
the agriculture sector in general and of poor farmers in particular.
Consequently, poverty increased among the landless, tenants, and small
farmers during the 1990s. In the absence of other alternative sources of
income, the poor families had to part with education and health and even
failed to purchase quality inputs timely which affected their
agricultural productivity adversely. As a result, the poverty situation
and the social status of the families have deteriorated further.

Various studies have emphasised the link between poverty and
agricultural growth using Indian data. The most important of them are
Ahluwalia (1978); Gaiha (1989); Datt and Ravallian (1998, 1998a); Fan,
Hazell and Thorat (2000) and De Janvry and Sadoulet (2002). These
studies concluded that there is an inverse relationship between
agricultural growth and rural poverty. A number of studies have also
been conducted to assess the incidence of poverty using data from
Pakistan, e.g., Naseem (1973); Mujahid (1978); Amjad and Irfan (1984);
Ahmad and Ludlow (1989); Ercelawn (1990); Malik (1991 and 1994); Gazdar,
et al. (1994); Anwar (1996, 1998); Amjad and Kemal (1997); Jafri (1999);
Arif, et al. (2000); FBS (2001) and World Bank (1995, 2002).

However, there is a dearth of empirical literature that analyses
the effects of poverty on the performance of agriculture. (3) An
exception is a study by Randrianarisoa and Minten (2001) that explores
the effects of rural poverty on agricultural production using data from
Madagascar. They estimate the primal production function at different
poverty levels and conclude that elasticities of production with respect
to inputs are different at poor and non-poor farms. The study concludes
that production elasticity of land is higher at the poor farms, and
therefore the redistribution of land from the rich to the poor farmers
may help in reducing poverty. Education, secure property rights, and
better rental arrangements enhance the poor farmer's agricultural
productivity, and thus these alleviate poverty.

So far, none of the studies has explored the link between
agricultural production and poverty in Pakistan. The present study
attempts to extend the work of Randrianarisoa and Minten (2001) and of
other authors (focusing on the nutrition and productivity relationship)
by estimating the frontier production (the best practice) function
incorporating different levels of poverty. The remaining paper consists
of three sections. Section 2 explains the data and the empirical
framework. Section 3 is devoted to results and discussion. The last
section concludes the paper and suggests some policy implications.

2. THE DATA AND THE METHODOLOGICAL FRAMEWORK

2.1. The Data

This study uses the 'Pakistan Rural Household Survey'
(PRHS) data collected by the Pakistan Institute of Development
Economics, Islamabad for the cropping year 2000-2001. This survey covers
16 districts in four provinces of the country. (4) No agricultural
household was observed in Gawadar District (in Balochistan). Two other
districts, Attock in Punjab and Dir in the NWFP, are predominantly
non-irrigated areas. Therefore, these three districts were excluded from
the production frontier analysis. The survey covers only the rural
areas, focusing on agriculture, credit, labour, and health issues. This
study uses only those farm households which grow crops, fruit, and
vegetables. These households in the overall sample are about 40 percent.
This PRHS survey covers information regarding crops output and inputs at
the plot-level. However, plot-level data were missing for a number of
variables including output. Therefore, such observations have to be
dropped from the analysis. Consequently, the total number of
observations finally used for estimating the stochastic production
frontier comes out to be 1566 regarding 1112 irrigated farms. (5)

2.2. Empirical Model

To quantify the impact of determinants of agricultural productivity
at different poverty levels in Pakistan, we use the primal production
frontier technique. Randrianarisoa and Minten (2001) is the only study
which estimates the production response coefficients at different
poverty levels using data from Madagascar. They applied an average
production function approach using conventional and nonconventional
variables in the model. The use of average production function
incorporating non-conventional (socioeconomic) variables in the
production function raises various questions. The inclusion of these
variables in the production function has been criticised on the ground
that they have 'roundabout' effects on production and, thus,
may not be included in the model [Kalirajan (1981)]. On the other hand,
the average production function, which is estimated using the OLS
technique, assumes that farmers are 100 percent technically efficient,
which may not be true and is considered to be a very strong assumption.
(6)

Various models have been developed by the researchers that
accommodate the concept of technical inefficiency on the part of farm
manager, including the parametric and non-parametric models; the former
uses specific functional form, while the latter does not. The parametric
models can be divided further into the deterministic and the stochastic
frontier models. The deterministic model assumes that any deviation from
the frontier is due to inefficiency, while the stochastic modelling
technique allows for statistical noise.

Aigner, Lovell, and Schmidt (1977) and Meeusen and Broeck (1977)
independently developed the stochastic frontier approach that decomposes
the error term into two components. One is symmetric that captures the
effects of those variables which are not under the control of the
producer, and the other is one-sided, representing management
inefficiency. Kalirajan (1981) proposed that the predicted technical
inefficiency effects then could be regressed on various observable
explanatory variables involving farmer or farm-specific
attributes/factors to examine the determinants of inefficiency. Various
applied researchers have used this two-step procedure. However, this
procedure has been criticised on the ground that it violates one of the
basic assumptions, that of 'identically independently distributed
technical inefficiency effects in the stochastic frontier'
[Battese, Malik and Gill (1996)]. Battese and Coelli (1993, 1995)
proposed one-stage modelling in which the technical inefficiency effects
are a function of various observable variables such as age, education,
access to extension services, etc.

The stochastic production frontier model incorporating inefficiency
effects can be written as:

Where [Y.sub.i] represents the possible production level for the
ith sample farm; f([X.sub.i]; [beta]) is a suitable function of the
vector, [X.sub.i], of inputs for the ith farm and a vector, [beta], of
unknown parameters--this paper uses Cobb-Douglas type function for the
analysis; [V.sub.i]s are assumed to be independent and identically
distributed normal random errors having mean zero and variance
[sigma][v.sup.2] and are also independently distributed of [U.sub.i];
and [U.sub.i]s are non-negative technical inefficiency effects
representing management factors and are assumed to be independently
distributed with mean [u.sub.i] and variance [[sigma].sup.2] [Battese,
Malik and Gill (1996)]. The ith farm exploits the full technological
production potential when the value of [U.sub.i] comes out to be equal
to zero, and the farmer is then producing at the production frontier
implying that the producer cannot produce above the production frontier.
The higher the value of [U.sub.i], the farther away is the farmer from
the production frontier, indicating greater operational inefficiency
[Drysdale, Kalirajan and Zhao (1995)].

According to Battese and Coelli (1993), the technical inefficiency
component [U.sub.i] is a function that can be written as

[U.sub.i] = [Z.sub.i][delta] + [w.sub.i] ... ... ... ... ... ...
(2)

Where [Z.sub.i] is vector of explanatory variables, [delta] is a
vector of unknown parameters to be estimated, and [w.sub.i] is an
unobservable random variable assuming truncated normal distribution with
mean zero and variance [[sigma].sup.2.sub.w], given that [U.sub.i] is
non-negative (i.e., [w.sub.i] [greater than or equal
to].--[Z.sub.i][delta]). The Z variables could be the farm- and
farmer-specific variables. The technical efficiency of production at the
ith farm ([TE.sub.i]) can be computed as

Where [Y.sub.i] is the observed farm output and [Y.sup.*.sub.i] is
maximum possible output using the given level of inputs.

Definitions of all the variables included in the estimated model
are given in Table 1. The dependent variable, [Q.sub.ij], is the
weighted index of output from all the k crops grown at the ith farm and
the jth plot. This can be computed as

The weights are [W.sub.ijk] = [S.sub.ijk/TV, where [S.sub.ijk] is
the share of kth crop value in total value of crops output (TV) grown at
the ith farm and the jth plot, and [Y.sub.ijk] is the quantity of kth
crop output at the ith farm and jth plot.

The model includes some conventional inputs like area of the plot
in kanals (7) (Land), fertiliser nutrients applied per plot (NPK), hired
labour cost (Hlabour), family labour (FL), (10) water used at each plot
from both rented in and own tubewell (Twater), herbicide and pesticide
expenditures incurred at each plot (Pest), and farmyard-manure (FYM)
used at each plot. To accommodate canal water source of irrigation, a
dummy variable DCANAL is used. (11) The possible impact of soil quality
is accounted for by using two variables, which are percentage area
waterlogged (%Wlogged) and percentage area affected by salinity/sodicity
(%Salinity). To see the impact of cropping pattern/double cropping on
farm output, two variables are introduced in the model that are
proportionate area under cotton (Cotton/Cropped Area) and proportionate
area of the plot under rice crop (Rice/Cropped Area). The reasons for
using these variables are threefold. First, sowing of subsequent crop,
particularly wheat, is delayed and this delay reduces its productivity.
Secondly, in cropping year 2000-2001, productivity of both cotton and
rice crops was lower than the normal years because of shortage of canal
water and attack of diseases and insects. Thirdly, both of these crops
consume the highest proportion of pesticides.

The model also includes two other variables, namely, the rented in
plots (Tenant) and the location of the plots at the watercourse (Dtail).
These variables are observed at the plot level and, therefore, make more
sense to be included in the main production function.

To assess the role of management factors on the production
performance, personal characteristics of the farmers like education of
the head of household (Education) and age of the head of household (Age)
are included in the model. The other variables, which are expected to be
influencing productivity performance are land fragmentation--number of
plots a household operates (Plots), the number of tractors owned
(Tractor), and the number of tubewells owned (Twell) by the household,
loan from institutional sources (Floans), and loan from
non-institutional sources (IFloans).

To see the impact of poverty on agricultural output and production
response coefficients of conventional inputs, we have divided the data
into three groups having an almost equal number of observations based on
per capita food expenditures of the households. Consistent with these
groups, three dummy variables are introduced in the model. These are
[P.sub.1]. [P.sub.2], and [P.sub.3] considered as very poor,
poor/transitively poor, and rich, respectively. (12) Table 1 clearly
demonstrates that household level use of inputs and the output they
produce are considerably different across household categories. The main
characteristics of these groups are: (1) there are relatively greater
problems of waterlogging and salinity on the lands of very poor groups;
(2) a greater proportion of poor farmers are located at the tail-end of
the watercourses, and most of them are tenants; (3) a poor farmer owns
less farm machinery and is a small farmer; (4) poor farmers are less
educated; and (5) poor farmers mainly depend on getting loans from
non-institutional sources, while the rich ones borrow more from the
institutional sources.

The sample mean of binary variables provided in the last three
columns of Table 1 is the proportions of the sample plots taking on
particular qualitative attributes. For example, about 80 percent, 73
percent, and 63 percent households did not use farmyard manure at the
plots belonging to very poor ([P.sub.1], poor ([P.sub.2]), and nonpoor
([P.sub.3]) farmers respectively; and about 35 percent, 34 percent, and
28 percent of the plots are located at the tail-end of the watercourse
of the very poor, poor, and non-poor farmers, respectively.

3. RESULTS AND DISCUSSION

3.1. Production Frontier Estimation and Hypotheses Testing

The' maximum likelihood estimates of the parameters of the
stochastic production frontier and inefficiency model are estimated
using Frontier 4.1. Before proceeding to examine the parameter estimates
of the production frontier, we need to investigate the validity of the
model used for the analysis. The results of the tests of hypotheses are
reported in Table 2. These tests are performed using generalised
likelihood-ratio statistics, LR, which is defined as: LR = -2
ln[L([H.sub.0])/L([H.sub.1])], where L([H.sub.0]) and L([H.sub.1]) are
the values of the log likelihood function under the specifications of
null and alternate hypotheses, respectively. The LR test statistic has
an asymptotic chi-square distribution with degrees of freedom equal to
the difference between the, number of parameters in the unrestricted and
restricted models.

The first hypothesis we tested is [H.sub.0]: [[delta].sub.1]= ... =
[[delta].sub.7]=0, which indicates that the farm-level technical
inefficiencies are not affected by the independent variables included in
the model (Model A). (13) This null hypothesis is accepted. Given the
result of this hypothesis, the error component model (Model B) without
technical inefficiency effects was estimated and the second null
hypothesis which was performed is [H.sub.0]: [gamma] = 0 and [mu] [not
equal to] 0. (14) This hypothesis is rejected, implying that the
technical inefficiency effects exist at the farm level and the
stochastic frontier production function with truncated normal
distribution is the appropriate model, to be used for further analysis.
The third test that was performed is [H.sub.0]: [mu]=0, which indicates
that the one-sided error term is half-normally distributed with mean
zero. This null hypothesis was again rejected, implying that the
one-sided error term does not have half-normal distribution with mean
zero.

The fourth null hypothesis that we tested is that [H.sub.0]:
[[alpha].sub.1]=[[beta].sub.1]=[[beta].sub.2]=[[beta].sub.3],
[[alpha].sub.2]=[[beta].sub.4]=[[beta].sub.5]=[[beta].sub.6],
[[alpha].sub.3]=[[beta].sub.9]=[[beta].sub.10]=[[beta].sub.11],
[[alpha].sub.4]=[[beta].sub.13]=[[beta].sub.14]=[[beta].sub.15],
[[alpha].sub.5]=[[beta].sub.17]=[[beta].sub.18]=[[beta].sub.19], and
[[alpha].sub.6]=[[beta].sub.20]=[[beta].sub.21]=[[beta].sub.22], which
specifies that the estimates of input elasticities of production do not
differ at different levels of poverty [Model B vs. Model C]. The test
rejects the specification of Model C. and therefore it is suitable to
estimate a model that allows the parameter estimates to vary across the
poor and non-poor household levels. Based on tests of hypotheses, we can
conclude that .the error component model (Model B) assuming truncated
normal distribution for the. one-sided error term is the most
appropriate model to be used for further analysis. (15)

3.2. Parameter Estimates of the Production Frontier and the Issue
of Poverty

The results of Model B and C are given in Table 3. In total 35
parameters were estimated in the stochastic, production-frontier model
(Model B), including 32 in the production frontier model, and three
parameters [[sigma].sup.2.sub.s], [gamma] and [mu] relate to variance of
the random variables, Vi and Ui. The parameter estimate of [gamma] is
0.11 and is statistically significant at the one percent level.

Out of 35 estimated parameters, 29 are statistically significant.
Among those 28 are significant at the five percent level and the one is
significant at the 10 percent level. The remaining six estimates are not
significant even at the 10 percent level of significance.

The coefficients of land at all the three levels--poor and
non-poor--are significant and Carry. positive signs as expected. The
coefficients that are the elasticities of production increase with the
increase in the well-being of the sampled farmers. Production elasticity
of land of rich farmers is higher by 13 percent and 28 percent than the
production elasticities of the very poor and poor groups of farmers,
respectively. This result stipulates that at the present level of inputs
use and other resources available to the poor farmers, increase in land
at their disposal may not increase the farm output. The average area per
plot on the poorer farmers' farms (33.90 kanals) is significantly
lower than the area the richer farmers have (39.94 kanals). So is the
status of the overall farm size--average farm size of the first group
(very poor) is 70.37 kanals; second group (poor) has a farm size of
77.83 kanals; and the rich have an average farm size of 82.63 kanals.
Two conclusions can be drawn from this result: (1) land is more
productive at the rich farms; and (2) land distribution using the notion
of land reforms in favour of poor/small farmers in the presence of
prevailing farm structure, rural infrastructure, and the rural
supporting institutions will not increase farm productivity and thus
would not help alleviate poverty among the poor farmers. Rather, with
the existing land-ownership, if the access of poor farmers to
agricultural services is ensured, the agricultural productivity can be
increased considerably, which in turn would help reduce poverty. The
coefficients of all the four fertiliser-related variables are
statistically significant and carry a positive sign. Fertiliser
elasticity declined from 0.29 at the very poor farmers' farm to
0.19 at the rich farmers' farm. This indicates that farm production
is more responsive to the use of fertiliser at the poor farms as
compared to the rich farms. The use of fertiliser per unit of land at
the poor farms is significantly lower than the use at richer farms.
Therefore, encouraging the use of chemical fertiliser can increase
agricultural productivity of the poor farmers. The coefficient of
'phosphate to total NPK ratio' suggests that improvement in
this increases farm productivity but the impact is nonsignificant. The
reason could be the less variation in the use of P/NPK ratio. Table 1,
in the previous section, shows that the average P/NPK ratio appeared to
be the same. Nonetheless, the use is not balanced since the present
P/NPK ratio is 1/4 against the recommended 1/2. This highlights the fact
that promoting greater and balanced use of fertiliser at all farms is
needed in order to increase production and thus raise the well-being of
the farming community.

All the parameter estimates relating to hired labour are
statistically significant at the one percent level. The elasticity
estimate shows that the magnitude is significantly lower at the richer
farms than the value at poor farms. The reasons could be that about 70
percent of the very poor farmers do not use any hired labour, and even
if they do, the use per plot is very small as compared to the use at the
farms belonging to the richer farmers. The parameter estimates of the
family labour are statistically significant and carry positive signs.
The magnitudes of the coefficients are positively associated with the
well-being of the farmers groups, e.g., the elasticity of production of
very poor farmers is 0.16 while it is 0.23 for the rich farmers.
However, the use of family labour is relatively less prevalent on rich
farms. These results foretell that the productivity of labour is higher
at the well-off farms. This may be due to the reason that the rich
farmers' farms are more mechanised, having greater capital-labour
ratio than that of the poor. One conclusion that we can definitely draw
from this result is that better functioning of labour markets and the
reallocation of labour and capital resources could improve farm
productivity at poorer farmers' fields.

All parameter estimates relating to water use from the tubewell
sources (rented and/or owned) are statistically significant at the one
percent level. The magnitudes of coefficients show that elasticity of
production at the farms of very poor farmers is slightly lower than that
at the farms of other groups. The water markets do not work efficiently
because tubewells are immobile capital and water use depends on the
availability of a water channel and downstream location of the plots.
The data show that only 14 percent of the very poor farmers use their
own tubewells for irrigation, while 32 percent of the rich group use
their own tubewell water. Consequently, increasing access to tubewell
water either through better functioning of water markets and or through
improved access to institutional credit for installation of tubewells
would help improve farm productivity of poor farmers and thus help in
reducing poverty. Table 1 indicates that the non-user percentage of
tubewell water on the poorer farms group is significantly higher than
the percentage of non-users in the richer farms group. Moreover, the
magnitude of water use per plot or per unit of land at the poorer
farmers' fields is almost half the use at the richer farmers'
fields.

The results show statistically significantly negative impact of
pesticide use on poor farms' output, while the coefficient of rich
farms is not statistically different from zero. As a matter of fact, the
major chunk of pesticides used goes to cotton and rice crops, While the
performance of both these crops was poor in the survey year because of
availability of canal irrigation water below the normal level and attack
of insects and pests. However, the richer, farmers have spent about
three times more on the purchase of pesticides than the expenditures
incurred by the poor farmers. As a consequence, the reduction in rich
farms' productivity due to attack of insects and pests was
negligible.

The coefficients of farmyard-manure (FYM) variables are not
significantly different from zero at 10 percent probability level. It
could be due to the reason that most of the times our farmers apply FYM
in an un-composed form and, therefore, it may not benefit the crops it
is being used for.

To account for the impact of canal water, a dummy variable is
included in the model. The parameter estimate is significant at the one
percent probability level. The result shows that the plots receiving
canal water are significantly more productive than those not receiving
any canal water. The other canal irrigation-related variable used in
the. model is a dummy variables of plots located at the tails of
watercourses. The parameter estimate of this variable is significant at
the one percent probability level and carries a negative sign. The
magnitude of the coefficient shows that the farmers located at the tail
of the watercourses produce about 22 percent less than the production of
their fellow farmers located at the middle and/or head of the
watercourses.

The data shows that the tail-enders use almost all inputs per unit
of land less than the farmers having land at the head and/or middle of
the watercourses mainly because of low availability of water (See
Annexure 2). (16) The use of tubewell water is, however, slightly higher
on the plots located at the tails of the watercourses in order to
supplement the shortage of canal water. The data further shows that
tail-enders have greater soil-related problems (waterlogging and
salinity/sodicity). The other pertinent characteristics of farmers
located at the tail are the following:

* their farms are of smaller size than those of their counterparts
located elsewhere;

* they have proportionately less area under rice but greater area
under cotton; and

* more importantly, a relatively greater proportion of these
farmers belongs to the very poor and poor farmer household categories
than that of their fellows located elsewhere.

The above facts clearly demonstrate that the lack of a policy to
compensate the tail-enders is perpetuating poverty and intensifying it
further through land degradation and lower productivity.

The coefficient of salinity variables is negative and
statistically, significant. The incidence of soil salinity/sodicity on
poor farms is higher than on the non-poor farms, implying lower
productivity on the plots of the poor than on the plots belonging to the
rich farmers. The coefficient of waterlogging variable is also negative,
and is however statistically non-significant. This weak inverse
relationship between output and the waterlogging problem could be due to
the fact that the cropping year 2000-01 was a bad year for agriculture
because of an unprecedented drought situation in the. country, and
therefore the plots affected by the waterlogging problem proved to be a
blessing in disguise, resulting in better crop harvests.

Nonetheless, the situation of waterlogging and salinity/sodicity
has continued to be a very serious problem in Pakistan. About 2.5
million hectares of land has a water depth of 0-5 feet [Pakistan
(2004)]. Such a level of water depth is considered to be disastrous for
agricultural production [Pakistan (1988) and Ahmad, Ahmad, and Gill
(1998)], and crop yields on such lands are one-fourth of those being
realised on the farms with a water depth of more than 10 feet [Javed
(1991); Nadeem (1989); Mustafa (1991)]. However, the problem of
waterlogging has decreased to some extent with the government efforts in
the lining of canals and watercourses and with the construction for
water drainage. The government seems committed to the lining of 100
percent watercourses in the country within the next few years. In
contrast, the status of salinity/sodicity is deteriorating in the
country. The total affected area with salinity is about 6.2 million
hectares. Out of this, about 4.3 million hectares is severely affected
by salinity/sodicity and about 80 percent of these lands are not even
being cultivated. The remaining 1.9 million hectares is from slightly to
moderately saline, producing significantly lower than the potential,
resulting in a loss of more than 21 billion rupees of GDP annually [PIDE
and PCST (2004)]. The poor farmers having a relatively greater area
affected by waterlogging and salinity are adversely affected and thus
are falling deeper into poverty. The data used in this study also shows
that the applications of almost all inputs per unit of land are
significantly lower on plots affected by the salinity and waterlogging
problems (Annexure 3). Moreover, both of these problems have a greater
incidence in rice-growing areas--since a higher proportion of affected
plots has been under the rice crop. Consequently, agricultural
productivity is low and the incidence of poverty is high among families
cultivating such lands in these areas. The above facts imply that the
land reclamation policy needs to be initiated with full intensity in
order to stop perpetuation of poverty and to reduce its severity in the
country.

The cropping year 2000-2001 was a bad year particularly for the
cotton and rice crops due to the shortage of water and attack of
diseases and insects. Consequently, the productivity of these crops was
lower and faced negative growth trends. It is a well-known phenomenon
that in both the cropping systems in Pakistan, the productivity of the
subsequent crops sown after rice or cotton is reduced significantly,
especially of the wheat crop, due mainly to its delayed sowing.
Empirical work shows that the system's productivity has tended to
decline in rice-growing areas of the Subcontinent [see Cassman and
Pingali (1993); Pingali, Hussain and Gerpacio (1997); Ahmad, Ahmad and
Gill (1998); Ahmad, Chaudhry and Iqbal (2002)]. To see the impact of
rice area on farm crop production, a variable defined as the ratio of
area under rice to the total cropped area at a particular plot is used.
The parameter estimate of the rice-cropped area ratio is negative and
statistically significant at the one percent level of probability,
indicating considerably lower productivity on plots where the
proportionate area under rice crop has been greater. Ahmad, Chaudhry,
and Iqbal (2002) reached similar conclusion using a completely different
data set. The proportionate area under rice in the first group of
farmers at the poverty scale is much more than the area under rice on
richer farms. It is about 26 percent, 22 percent, and 14 percent,
respectively, on very poor, poor, and rich farmers' farms. In
addition to this result, sufficient evidence exists to show that the
cropping system where the rice is being grown extensively is resulting
in degradation and depletion of land resources [Cassman and Pingali
(1993); Pingali, Husain and Gerpacio (1997); Ahmad, Ahmad and Gill
(1998)]. This is turning out to be a serious threat in ensuring
sustainability of the rice-wheat cropping system in Pakistan.

The parameter estimate of the ratio of cotton area to the total
cropped area is also negative and statistically significant at the one
percent level; the greater the proportionate area under cotton, the less
the overall farm incomes. It is a well-known fact that the harvesting
season of cotton crop and the sowing timings of wheat overlap.
Consequently, wheat-sowing in cotton fields is delayed and results in
reduced wheat productivity.

The tenancy variable turned out to be positive and significant at
the one percent probability level. This implies that the farmers realise
more of the potential output from the rented in plots. (17) For the
tenants, insecurity and financial difficulties are the key factors
discouraging investment in more productive enterprising activities like
improvements in land and managerial capabilities. Moreover, the tenants
generally cultivate small landholdings and are often under financial
stress, like paying rent/share, facing high variable costs, and saving
something for the family's survival. As a result, the tenants tend
to struggle more in achieving a higher production potential. Another
main reason of higher productivity at the tenants' plots is that
the rented in plots are of better soil quality,: and are less affected
by waterlogging and salinity (See Annexure 2).

3.3. Technical Efficiencies of Farmers

The technical efficiencies (TE) of the sampled farmers were
obtained using Equation 3. As mentioned earlier, the technical
inefficiency effects are significant; thus the technical efficiencies of
sampled farmers are less than one. The cost accrued to the farmers due
to the existence of technical inefficiencies is .huge, ranging from 17
percent to 62 percent in terms of loss in output. The un-shaded area in
Figure 1 indicates the technical inefficiency, while the shaded area
represents the technical efficiency. The un-shaded area amounts to 43
percent loss in output on the average due to technical inefficiency.

[FIGURE 1 OMITTED]

Table 4 shows that the least efficient group has TE equal to 0.49,
implying that the least efficient group realises only 49 percent of the
actual potential in agriculture, while the upper 20 percent on the
efficiency scale realises 67 percent of the potential ,output. One major
conclusion that can be drawn from the indicators, given in .Table 4 is
that the least efficient group is not only operating significantly below
the frontier but also operates at the lower portion of the production
frontier. Table 4 further reveals that the least efficient group
includes a greater proportion of poor farmers with a greater problem of
waterlogging and salinity, less farm machinery, and low access to
credit. Moreover, the least efficient group owns a lower number of
livestock units, and a relatively greater number of farmers is located
at the tail-ends of the watercourses. Enhanced access to the inputs,
soil conservation technologies, agricultural credit, etc., would likely
raise* agricultural output, both along the production function and
improvement in total factor productivity, (18) particularly at the
fields of the poor farmers.

4. CONCLUSION AND POLICY IMPLICATIONS

The results of the production frontier analysis show that the input
elasticities of production are different at different levels of poverty.
Production elasticity of land is about 28 percent higher at the rich
farms than that at farms of the poorest group. However, elasticities of
production with respect to fertiliser, hired labour, and water are
greater at the poor farms. The data show that the use of these inputs
per unit of land at the poor farmers' farms is considerably lower
than the use at the rich farms. The salinity/sodicity-problem is
adversely affecting the farm productivity and efficiency, particularly
at the .poor farmers' farms. This, in turn, affects their ownership
of other assets like farm machinery and livestock.

Farm productivity is negatively associated with the increase in
proportionate area under rice crop. Moreover, sufficient evidence exists
that in the cropping system where the rice is being grown extensively,
the lands are being degraded and depleted. The data used in this study
show that a considerably high percentage of total cropped area was
allocated to rice crop on poor farms in cropping year 2000-01. The
results also show that farm output is negatively associated with the
greater proportionate area sown under cotton during the survey year
mainly because of drought in the country and insect and pest attack.

Further, the results suggest that the rented in plots yield higher
output. It may be due to the fact that these plots are of better soil
quality, relatively less affected by waterlogging and salinity. The
results reveal that the plots located at the tail-ends are significantly
less productive as compared to the plots situated at the middle and/or
head of the watercourses. The tail-enders use almost all inputs per unit
of land, except tubewell water, but less than their counterparts, and
have greater problems of waterlogging and salinity/sodicity.

The average cost accrued to the farmers due to the existence of
technical inefficiencies is about 43 percent in terms of loss in output,
with wide variations ranging from 17 percent to 62 percent. The input
use is higher at the more efficient farms as compared to that on the
inefficient farms. The least efficient group includes a greater
proportion of poor farmers, who have a greater problem of waterlogging
and salinity, have low access to credit, own less farm machinery, own a
lower number of livestock units, and are more frequently located at the
tail-end of the watercourses.

An important conclusion of the efficiency analysis is that the
least efficient group is not only operating significantly below the
frontier but also operates at the lower portion of the production
frontier. The land is more productive at the rich farms. This implies
that following a simple land distribution mechanism--using the notion of
land reforms in favour of poor/small farmers--in the presence of
prevailing farm structure, rural infrastructure, and weak
farm-supporting institutions may not increase farm productivity and thus
would not help alleviate poverty in rural areas. The results call for a
strong and active role of the government in close partnership with the
private sector in the rural areas in initiating income-generating
activities both for the farm and non-farm poor households to break the
vicious circle of poverty, land degradation, and low agricultural
productivity. It is strongly felt that there is a need to establish
agri-malls, possibly in joint private-public partnership, or encouraging
the private sector by providing incentives like loans on attractive
terms and conditions, better infrastructure, and other facilities to
establish such type of businesses to put a stop to linearly rising
poverty (which has an almost one-to-one relationship with low
agricultural productivity). Such activities would improve access to
inputs that would be an effective way to improve agricultural
productivity and to reduce poverty. There is also a need to support and
strengthen the non-farm sector to generate employment.

The interlocking of land degradation and poverty necessitates a
land reclamation policy. Moreover, the area allocated to rice crop,
particularly for coarse varieties, needs to be rationalised where the
country has no comparative advantage. It is also required that
cultivation of legumes and use of green manuring be promoted to restore
soil fertility in affected areas. It is also imperative to have a gypsum
use policy in the country, besides the use of fertiliser.

The results demonstrate that there is a lack of policy to
compensate the tailenders. Current practices are perpetuating poverty
and intensifying it further through land degradation and lower
productivity. Therefore, investment by the Irrigation Department as well
as by the farmers or their organisations in desilting and lining of
canals/watercourses may play an important role in increasing
agricultural productivity and thus reducing poverty.

Author's Note: I am grateful to Dr A. R. Kemal, Director,
PIDE, Dr Muhammad Iqbal, Senior Research Economist, PIDE, and Dr Bashir
Ahmad, Vice-Chancellor, University of Agriculture, Faisalabad, for
technical discussions with them and for their comments on an earlier
draft of this paper. I am also thankful to an anonymous referee of this
journal for valuable comments and suggestions for improvements in an
earlier version of this paper.

Croppenstedt, A., and C. Muller (2000) The Impact of Farmers'
Health and Nutritional Status on their Productivity and Efficiency:
Evidence from Ethiopia. Economic Development and Cultural Change 48:3,
475-502.

Javed, M. (1989) Impact of Different Water Table Depths With
Special Reference to Rice Growing Areas of Sheikhupura District. M.Sc.
Thesis, Department of Agricultural Economics, University of Agriculture,
Faisalabad.

Saris, A. (2001) The Role of Agriculture in Economic Development
and Poverty Reduction: An Empirical and Conceptual Foundation. Rural
Development Department, World Bank, Washington, D.C. (Rural Strategy
Background Paper No. 2.)

Schiff, M., and A. Valdes (1991) The Political Economy of
Agricultural Price Policy. Vol. 4. A Synthesis of the Economics of
Developing Countries. Baltimore: The Johns Hopkins University Press.

Munir Ahmad is Chief of Research at the Pakistan Institute of
Development Economics, Islamabad.

(1) According to the recent agricultural census [Pakistan (2003)],
about 58 percent of the farms having less than 5 acres cultivate only 16
percent of the total area, while 5 percent of the farms having 25 acres
or above cultivate 37 percent of the total area in Pakistan. In Sindh,
about 46 percent of the farms having lands less than 5 acres cultivate
only 12 percent of the area, while large farms ([greater than or equal
to] 25 acres) are only 7 percent, cultivating the major chunk of the
area, i.e., 44 percent. In the NWFP, 79 percent of the farms having less
than 5 acres cultivate 32 percent of the area, while only one percent of
the farms having 25 acres or more cultivate 27 percent of the area. Land
distribution in Balochistan shows that 29 percent are marginal farms
(<5 acres) cultivating 3 percent of the area, while 16 percent are
the large farms ([greater than or equal to] 25 acres) cultivating 63
percent of the total area.

(2) Causality in poverty-agricultural productivity relationship
runs both ways. A high level of poverty results in lower productivity
because of low use of inputs caused by financial constraints, and in low
labour productivity because of low calorie intake and poor health, etc.
On the other hand, low agricultural productivity leads to lower income
that in turn affects the poverty. The relationship can be studied using
the simultaneous equation model. However, the data at hand cannot be
used for such a study. Moreover, the major objective of this study is to
estimate elasticities of production at the poor and the non-poor farms.

(3) Various studies are found in the literature exploring the link
between health, nutrition, and productivity. The pioneering studies
include Leibenstein (1957); Stiglitz (1976) and Bliss and Stern (1978).
The subsequent studies include Baldwin and Weisbrod (1974); Weisbrod and
Helminiak (1977); among others. Applications are also found in the
agriculture sector and provide mixed results. The list includes the work
published by Pitt and Rosenweig (1986); Strauss (1986); Deolalikar
(1988); Fafchamps and Quisumbing (1997); Haddad and Bouis (1991);
Behrman and Deolalikar (1989); Foster and Rosenweig (1993); Thomas and
Straus (1997) and Bhargava (1997), among others. However, no
comprehensive work has been done using data from Pakistan.

(4) The data covers six districts in Punjab, which are Faisalabad,
Attock, Hafizabad, Vehari, Muzaffargarh, and Bahawalpur. Four districts,
including Badin, Nawabshah, Mirpur Khas, and Larkana, were selected from
Sindh province; three districts, including Dir, Mardan and Lakimarwat
belong to the NWFP; and three districts, Loralai, Khuzdar and Gawadar,
were from Balochistan. In total 23 Tehsils (sub-district) were covered
in all the districts. One Tehsil was selected from each district, except
Faisalabad (4 Tehsils), Attock (2 Tesils), Badin (2 Tesils), Dir (2
Tesils), and Larkana (2 Tesils). Total villages covered in all Tehsils
were 151, varying from one to 10 in each Tehsil. The total number of
households covered under this survey was 2726, including both farm and
non-farm households.

(5) The numbers of plots they cultivate vary from 1 to 6.

(6) The concept of technical efficiency of a firm was first
introduced by Farrell in his pioneering work published in 1957. He
defined it as the ratio of realised output to that of maximum achievable
potential with the same level of inputs and technology.

(7) Kanal is equal to 1/8th of an acre.

(8) Averages of all variables are in original units, not in logs.

(9) Following Battese (1997), dummies for variables have also been
used that have the zero value in the data to account for different
production regimes for farmers who use certain inputs, relative to those
who do not. Failing to do so results in biased parameter estimates of
the production function using the Cobb-Douglas/Translog functional
forms.

(10) Actual family labour given in the survey was not used in the
model due to two reasons. First, the information obtained in the survey
was at the household level; and secondly, the data on family labour
seemed to be abnormal and many observations were missing. There are
various observations where there was neither family labour nor hired
labour reported. Therefore, it was decided to use male members of a
household between ages of 14 years to 70 years as a proxy for permanent
labour.

(11) Data on the number of turns and hours per turn were available.
However, it appeared that the information on this variable is also very
crude.

(12) Average food expenditures per person for very poor, poor, and
rich farm categories are observed as Rs 476.66, Rs 797.4, and Rs
1838.71, respectively.

(13) The maximum likelihood estimates of Model A are presented
in-Annexure 1.